Synthetic data is used in a wide variety of fields and systems, including public health systems, financial systems, environmental monitoring systems, product development systems, and other systems. Synthetic data includes, for example, anonymized actual data or fake data. Synthetic data may be needed where actual data reflecting real-world conditions, events, and/or measurements are unavailable or where confidentiality is required. Synthetic data may be used in methods of data compression to create or recreate a realistic, larger-scale data set from a smaller, compressed dataset (e.g., as in image or video compression). Synthetic data may be desirable or needed for multidimensional datasets (e.g., data with more than three dimensions).
Conventional systems and methods of generating synthetic time-series data generally suffer from deficiencies. For example, conventional approaches may be limited to generating synthetic data in a small number of dimensions (e.g., two-dimensional image data), but be unable to generate time-series data for higher-dimensional datasets (e.g., environmental data with multiple, interdependent variables). Conventional approaches may provide limited methods of generating synthetic time series (e.g., systems may be limited to random walks). Further, conventional approaches may be unable to produce synthetic data that realistically captures changes in data values over time (e.g., conventional approaches may produce unrealistic video motion). In addition, conventional systems and methods for generating synthetic data are computationally intensive. Some approaches require use of large datasets, rather than descriptors of large datasets, which inefficiently use computer resources. For example, conventional methods that tokenize or otherwise anonymize sensitive fields (e.g., personal identifiers) may require large data volumes, similar in size to an original, untokenized dataset.
Therefore, in view of the shortcomings and problems with conventional approaches to generating synthetic time-series data, there is a need for accurate, unconventional approaches that generate realistic synthetic multidimensional time-series data.